Overview

Dataset statistics

Number of variables12
Number of observations2968
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.4 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly overall correlated with qty_invoices and 3 other fieldsHigh correlation
recency_days is highly overall correlated with qty_invoicesHigh correlation
qty_invoices is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qty_items is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qty_products is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
avg_basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly overall correlated with qty_products and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 25.1569664)Skewed
frequency is highly skewed (γ1 = 24.87687084)Skewed
qty_returns is highly skewed (γ1 = 21.9754032)Skewed
customer_id has unique valuesUnique
recency_days has 33 (1.1%) zerosZeros
qty_returns has 1481 (49.9%) zerosZeros

Reproduction

Analysis started2023-03-16 22:45:08.849755
Analysis finished2023-03-16 22:45:27.493414
Duration18.64 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.377
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-16T19:45:27.597813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.35
Q113798.75
median15220.5
Q316768.5
95-th percentile17964.65
Maximum18287
Range5940
Interquartile range (IQR)2969.75

Descriptive statistics

Standard deviation1719.1445
Coefficient of variation (CV)0.11258036
Kurtosis-1.2061782
Mean15270.377
Median Absolute Deviation (MAD)1489
Skewness0.032193711
Sum45322479
Variance2955457.9
MonotonicityNot monotonic
2023-03-16T19:45:27.735646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
12670 1
 
< 0.1%
17734 1
 
< 0.1%
14905 1
 
< 0.1%
16103 1
 
< 0.1%
14626 1
 
< 0.1%
14868 1
 
< 0.1%
18246 1
 
< 0.1%
17115 1
 
< 0.1%
16611 1
 
< 0.1%
Other values (2958) 2958
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

Distinct2953
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2693.4851
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-16T19:45:27.875920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.7325
Q1570.845
median1085.51
Q32306.905
95-th percentile7169.562
Maximum279138.02
Range279131.82
Interquartile range (IQR)1736.06

Descriptive statistics

Standard deviation10135.465
Coefficient of variation (CV)3.7629558
Kurtosis397.30132
Mean2693.4851
Median Absolute Deviation (MAD)670.84
Skewness17.635372
Sum7994263.7
Variance1.0272766 × 108
MonotonicityNot monotonic
2023-03-16T19:45:28.006037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1078.96 2
 
0.1%
2053.02 2
 
0.1%
331 2
 
0.1%
1353.74 2
 
0.1%
889.93 2
 
0.1%
745.06 2
 
0.1%
379.65 2
 
0.1%
2092.32 2
 
0.1%
731.9 2
 
0.1%
734.94 2
 
0.1%
Other values (2943) 2948
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%
65039.62 1
< 0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.309299
Minimum0
Maximum373
Zeros33
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-16T19:45:28.145838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.760922
Coefficient of variation (CV)1.2091707
Kurtosis2.7765172
Mean64.309299
Median Absolute Deviation (MAD)26
Skewness1.7980529
Sum190870
Variance6046.7611
MonotonicityNot monotonic
2023-03-16T19:45:28.288098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
2 85
 
2.9%
3 85
 
2.9%
8 76
 
2.6%
10 67
 
2.3%
9 66
 
2.2%
7 66
 
2.2%
17 64
 
2.2%
16 55
 
1.9%
Other values (262) 2218
74.7%
ValueCountFrequency (%)
0 33
 
1.1%
1 99
3.3%
2 85
2.9%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

qty_invoices
Real number (ℝ)

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7243935
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-16T19:45:28.459076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8577599
Coefficient of variation (CV)1.5473709
Kurtosis190.78624
Mean5.7243935
Median Absolute Deviation (MAD)2
Skewness10.765555
Sum16990
Variance78.45991
MonotonicityNot monotonic
2023-03-16T19:45:28.624903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 784
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
1 190
 
6.4%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 190
 
6.4%
2 784
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

qty_items
Real number (ℝ)

Distinct1670
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1582.1044
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-16T19:45:28.773279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.35
Q1296
median640
Q31399.5
95-th percentile4403.25
Maximum196844
Range196843
Interquartile range (IQR)1103.5

Descriptive statistics

Standard deviation5705.2914
Coefficient of variation (CV)3.6061408
Kurtosis516.7418
Mean1582.1044
Median Absolute Deviation (MAD)421
Skewness18.737654
Sum4695686
Variance32550350
MonotonicityNot monotonic
2023-03-16T19:45:28.916690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
150 9
 
0.3%
88 9
 
0.3%
246 8
 
0.3%
272 8
 
0.3%
84 8
 
0.3%
260 8
 
0.3%
288 8
 
0.3%
1200 7
 
0.2%
516 7
 
0.2%
Other values (1660) 2885
97.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 2
0.1%
12 2
0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80263 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%
57885 1
< 0.1%
50255 1
< 0.1%

qty_products
Real number (ℝ)

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.76449
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-16T19:45:29.066922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.93294
Coefficient of variation (CV)2.1987868
Kurtosis354.77884
Mean122.76449
Median Absolute Deviation (MAD)44
Skewness15.706135
Sum364365
Variance72863.79
MonotonicityNot monotonic
2023-03-16T19:45:29.203650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 43
 
1.4%
20 37
 
1.2%
35 35
 
1.2%
29 35
 
1.2%
19 34
 
1.1%
15 33
 
1.1%
11 32
 
1.1%
26 31
 
1.0%
27 30
 
1.0%
25 30
 
1.0%
Other values (458) 2628
88.5%
ValueCountFrequency (%)
1 6
 
0.2%
2 14
0.5%
3 15
0.5%
4 17
0.6%
5 26
0.9%
6 29
1.0%
7 18
0.6%
8 19
0.6%
9 26
0.9%
10 28
0.9%
ValueCountFrequency (%)
7838 1
< 0.1%
5673 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1637 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2965
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.994257
Minimum2.1505882
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-16T19:45:29.352965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.915888
Q113.118111
median17.953447
Q324.981794
95-th percentile90.052125
Maximum4453.43
Range4451.2794
Interquartile range (IQR)11.863683

Descriptive statistics

Standard deviation119.53207
Coefficient of variation (CV)3.6228143
Kurtosis812.96474
Mean32.994257
Median Absolute Deviation (MAD)5.9790186
Skewness25.156966
Sum97926.954
Variance14287.915
MonotonicityNot monotonic
2023-03-16T19:45:29.483665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2
 
0.1%
4.162 2
 
0.1%
14.47833333 2
 
0.1%
18.15222222 1
 
< 0.1%
13.92736842 1
 
< 0.1%
36.24411765 1
 
< 0.1%
29.78416667 1
 
< 0.1%
22.8792623 1
 
< 0.1%
20.51104167 1
 
< 0.1%
149.025 1
 
< 0.1%
Other values (2955) 2955
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%
615.75 1
< 0.1%

avg_recency_days
Real number (ℝ)

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-67.302133
Minimum-366
Maximum-1
Zeros0
Zeros (%)0.0%
Negative2968
Negative (%)100.0%
Memory size46.4 KiB
2023-03-16T19:45:29.616364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-366
5-th percentile-200.65
Q1-85.333333
median-48.267857
Q3-25.917308
95-th percentile-8
Maximum-1
Range365
Interquartile range (IQR)59.416026

Descriptive statistics

Standard deviation63.505358
Coefficient of variation (CV)-0.94358612
Kurtosis4.9080488
Mean-67.302133
Median Absolute Deviation (MAD)26.267857
Skewness-2.066084
Sum-199752.73
Variance4032.9306
MonotonicityNot monotonic
2023-03-16T19:45:29.747869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-14 25
 
0.8%
-4 22
 
0.7%
-70 21
 
0.7%
-7 20
 
0.7%
-35 19
 
0.6%
-49 18
 
0.6%
-11 17
 
0.6%
-46 17
 
0.6%
-21 17
 
0.6%
-28 16
 
0.5%
Other values (1248) 2776
93.5%
ValueCountFrequency (%)
-366 1
 
< 0.1%
-365 1
 
< 0.1%
-363 1
 
< 0.1%
-362 1
 
< 0.1%
-357 2
0.1%
-356 1
 
< 0.1%
-355 2
0.1%
-352 1
 
< 0.1%
-351 2
0.1%
-350 3
0.1%
ValueCountFrequency (%)
-1 16
0.5%
-1.5 1
 
< 0.1%
-2 13
0.4%
-2.5 1
 
< 0.1%
-2.601398601 1
 
< 0.1%
-3 15
0.5%
-3.321428571 1
 
< 0.1%
-3.330357143 1
 
< 0.1%
-3.5 2
 
0.1%
-4 22
0.7%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11383237
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-16T19:45:29.893287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088935048
Q10.016339869
median0.025898352
Q30.049478583
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.033138713

Descriptive statistics

Standard deviation0.40822056
Coefficient of variation (CV)3.5861552
Kurtosis989.06632
Mean0.11383237
Median Absolute Deviation (MAD)0.012196886
Skewness24.876871
Sum337.85449
Variance0.16664402
MonotonicityNot monotonic
2023-03-16T19:45:30.027928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 198
 
6.7%
0.0625 18
 
0.6%
0.02777777778 17
 
0.6%
0.02380952381 16
 
0.5%
0.09090909091 15
 
0.5%
0.08333333333 15
 
0.5%
0.03448275862 14
 
0.5%
0.02941176471 14
 
0.5%
0.03571428571 13
 
0.4%
0.07692307692 13
 
0.4%
Other values (1215) 2635
88.8%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1.142857143 1
 
< 0.1%
1 198
6.7%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%
0.5 3
 
0.1%

qty_returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct213
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.888477
Minimum0
Maximum9014
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-16T19:45:30.176213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation282.86478
Coefficient of variation (CV)8.107685
Kurtosis596.20199
Mean34.888477
Median Absolute Deviation (MAD)1
Skewness21.975403
Sum103549
Variance80012.486
MonotonicityNot monotonic
2023-03-16T19:45:30.306993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
7 43
 
1.4%
8 43
 
1.4%
Other values (203) 705
23.8%
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%
1594 1
< 0.1%

avg_basket_size
Real number (ℝ)

Distinct1978
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.25289
Minimum1
Maximum6009.3333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-16T19:45:30.438494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.2375
median172.29167
Q3281.54808
95-th percentile599.58
Maximum6009.3333
Range6008.3333
Interquartile range (IQR)178.31058

Descriptive statistics

Standard deviation283.8932
Coefficient of variation (CV)1.2016496
Kurtosis102.78169
Mean236.25289
Median Absolute Deviation (MAD)83.041667
Skewness7.7018777
Sum701198.57
Variance80595.347
MonotonicityNot monotonic
2023-03-16T19:45:30.567591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
82 9
 
0.3%
86 9
 
0.3%
73 9
 
0.3%
136 8
 
0.3%
75 8
 
0.3%
60 8
 
0.3%
88 8
 
0.3%
130 7
 
0.2%
Other values (1968) 2881
97.1%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%
2082.225806 1
< 0.1%

avg_unique_basket_size
Real number (ℝ)

Distinct906
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.489977
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-16T19:45:30.708873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.6666667
median13.6
Q322.144643
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.477976

Descriptive statistics

Standard deviation15.460127
Coefficient of variation (CV)0.88394209
Kurtosis29.324685
Mean17.489977
Median Absolute Deviation (MAD)6.6
Skewness3.4364678
Sum51910.252
Variance239.01552
MonotonicityNot monotonic
2023-03-16T19:45:30.849189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 42
 
1.4%
9 41
 
1.4%
8 39
 
1.3%
16 39
 
1.3%
17 38
 
1.3%
14 38
 
1.3%
11 36
 
1.2%
5 36
 
1.2%
7 36
 
1.2%
15 35
 
1.2%
Other values (896) 2588
87.2%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 6
0.2%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 12
0.4%
0.5454545455 1
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.6176470588 1
 
< 0.1%
ValueCountFrequency (%)
259 1
< 0.1%
177 1
< 0.1%
148 1
< 0.1%
127 1
< 0.1%
105 1
< 0.1%
104 1
< 0.1%
101 1
< 0.1%
98 1
< 0.1%
95.5 1
< 0.1%
94.33333333 1
< 0.1%

Interactions

2023-03-16T19:45:25.441192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:09.118456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:10.557014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:12.060283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:13.526434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:14.911541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:16.558110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:18.061994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:19.402750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:21.074984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:22.527003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:23.882089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:25.556891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:09.253827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:10.667607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:12.176573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:13.638041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:15.040350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:16.682023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:18.182638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:19.520527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:21.195947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:22.635092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:23.996846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:25.668099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:09.367326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:10.777129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:12.290959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:13.746960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:15.311894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:16.800181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:18.293856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:19.637752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:21.314045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:22.743064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:24.111901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:26.020423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:09.492230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:10.893658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:12.412198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:13.861712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:15.441747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:16.930862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:18.413201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:19.948181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:21.442141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:22.858086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:24.233538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:26.126335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:09.601463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:11.001170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:12.519572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:13.963419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:15.559566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:17.045120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:18.516891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:20.066068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:21.553116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:22.964005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:24.344139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:26.256046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:09.728390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:11.123623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:12.646945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:14.087780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:15.689850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:17.185862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:18.636855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:20.200379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:21.683528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:23.087590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:24.477247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:26.385837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:09.850178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:11.369016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:12.779719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:14.213032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:15.825091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:17.328162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:18.755597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:20.337912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:21.813347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:23.211082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:24.606114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:26.497476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:09.956956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:11.475212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:12.893099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:14.319916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:15.939910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:17.444001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:18.853773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:20.450644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:21.937498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:23.321553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:24.757368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:26.623242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:10.077363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:11.593943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:13.026168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:14.455255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:16.072310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:17.573822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:18.971093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:20.581018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:22.062860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:23.439972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:24.933967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:26.755396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:10.219986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:11.716204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:13.163570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:14.575158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:16.199372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:17.702060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:19.085951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:20.711498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:22.182115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:23.554934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:25.098017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:26.875877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:10.328725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:11.823743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:13.280438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:14.682451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:16.312539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:17.819653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:19.185166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:20.827012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:22.291302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:23.660141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:25.208078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:26.995062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:10.444979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:11.946028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:13.408203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:14.801189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:16.438821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:17.942441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:19.295917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:20.951833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:22.411243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:23.772832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T19:45:25.324832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-03-16T19:45:30.972356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
customer_idgross_revenuerecency_daysqty_invoicesqty_itemsqty_productsavg_ticketavg_recency_daysfrequencyqty_returnsavg_basket_sizeavg_unique_basket_size
customer_id1.000-0.0770.0010.026-0.0710.013-0.131-0.019-0.002-0.064-0.123-0.016
gross_revenue-0.0771.000-0.4140.7720.9250.7460.2450.2490.0910.3710.5740.106
recency_days0.001-0.4141.000-0.503-0.407-0.4360.049-0.1090.017-0.119-0.0970.014
qty_invoices0.0260.772-0.5031.0000.7180.6900.0600.2580.0780.2950.101-0.181
qty_items-0.0710.925-0.4070.7181.0000.7320.1660.2280.0810.3430.7290.148
qty_products0.0130.746-0.4360.6900.7321.000-0.3770.1650.0350.2440.3840.515
avg_ticket-0.1310.2450.0490.0600.166-0.3771.0000.1230.0910.1890.187-0.618
avg_recency_days-0.0190.249-0.1090.2580.2280.1650.1231.0000.8810.3980.078-0.131
frequency-0.0020.0910.0170.0780.0810.0350.0910.8811.0000.2350.028-0.122
qty_returns-0.0640.371-0.1190.2950.3430.2440.1890.3980.2351.0000.209-0.053
avg_basket_size-0.1230.574-0.0970.1010.7290.3840.1870.0780.0280.2091.0000.404
avg_unique_basket_size-0.0160.1060.014-0.1810.1480.515-0.618-0.131-0.122-0.0530.4041.000

Missing values

2023-03-16T19:45:27.161789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-16T19:45:27.385842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysqty_invoicesqty_itemsqty_productsavg_ticketavg_recency_daysfrequencyqty_returnsavg_basket_sizeavg_unique_basket_size
0178505391.21372.034.01733.0297.018.152222-35.50000017.00000040.050.9705880.617647
1130473232.5956.09.01390.0171.018.904035-27.2500000.02830235.0154.44444411.666667
2125836705.382.015.05028.0232.028.902500-23.1875000.04032350.0335.2000007.600000
313748948.2595.05.0439.028.033.866071-92.6666670.0179210.087.8000004.800000
415100876.00333.03.080.03.0292.000000-8.6000000.07317122.026.6666670.333333
5152914623.3025.014.02102.0102.045.326471-23.2000000.04011529.0150.1428574.357143
6146885630.877.021.03621.0327.017.219786-18.3000000.057221399.0172.4285717.047619
7178095411.9116.012.02057.061.088.719836-35.7000000.03352041.0171.4166673.833333
81531160767.900.091.038194.02379.025.543464-4.1444440.243316474.0419.7142866.230769
9160982005.6387.07.0613.067.029.934776-47.6666670.0243900.087.5714294.857143
customer_idgross_revenuerecency_daysqty_invoicesqty_itemsqty_productsavg_ticketavg_recency_daysfrequencyqty_returnsavg_basket_sizeavg_unique_basket_size
5626177271060.2515.01.0645.066.016.064394-6.01.0000006.0645.00000066.000000
563617232421.522.02.0203.036.011.708889-12.00.1538460.0101.50000015.000000
563717468137.0010.02.0116.05.027.400000-4.00.4000000.058.0000002.500000
564813596697.045.02.0406.0166.04.199036-7.00.2500000.0203.00000066.500000
5654148931237.859.02.0799.073.016.956849-2.00.6666670.0399.50000036.000000
565812479473.2011.01.0382.030.015.773333-4.01.00000034.0382.00000030.000000
567914126706.137.03.0508.015.047.075333-3.00.75000050.0169.3333334.666667
5685135211092.391.03.0733.0435.02.511241-4.50.3000000.0244.333333104.000000
569515060301.848.04.0262.0120.02.515333-1.02.0000000.065.50000020.000000
571412558269.967.01.0196.011.024.541818-6.01.000000196.0196.00000011.000000